Data Science for Community management

We will present some typical community management worries about community health, productivity and visibility and how some open source tools could be used to face them. Open source development transparency allows community to behave as data scientists and extract valuable information.

During last years, we have seen the growth of an specific position in many companies and OSS foundations. You might have seen them as “community managers”, “developer advocates”, “developer relations”, etc. All of them share some goals / responsibilities in common with their communities: health, productivity and visibility. How to achieve them?

It has been said that “without data, you are another person with an opinion”, and management shouldn’t rely in opinions but facts. This path increases transparency, trust and reduces conflicts, since usually “numbers speak louder than opinions”.

If we look around in our daily live many of us are already working with tools for similar goals in different areas of life. We can wear heart rate monitors, check our activity in github or jira and play with zillions of marketing campaigning tools.

In OSS communities people “contribute” for a goal. So, we should start by looking at where people are contributing, and here starts the “community manager nightmare”. People contribute in a lot of places: github, gerrit, jira, mailing lists, slack, meetup, etc. It seems obvious which data sources we have and since most of us are technical people, we might build our own tools for tracking them. Have you already gone through that path? Some of our best friends have gone, wasting 80% taking data instead of taking decisions. Lots of places to look at, mainly silos of data you need to massage to get information out of them, and limited amount of time understand and manage your project.

During this talk, we will present some community managers typical questions and how tools OSS, like for example GrimoireLab, could be used to answer them, allowing the community to behave as data scientists extracting valuable information from within and be a data driven community.

These kind of tools represent a new ecosystem for “Open Development Analytics”, where communities can (and probably should) rely on open tools to ensure transparency on such important process like “measuring the community”.